<![CDATA[The utilization of sales transaction data can help businesses identify consumer purchasing patterns and support business decision-making. Lalan Mart has sales transaction data that has not been optimally utilized to identify relationships among products that are frequently purchased together. This study aims to analyze consumer purchasing patterns using the Apriori Algorithm and provide recommendations to support inventory management, product placement, and promotional strategies. The data consisted of 4,740 transactions involving 157 types of products during the period from January to December 2025. The analysis was conducted using the Apriori Algorithm with a minimum support of 6% and a minimum confidence of 60%. The results produced 11 association rules with lift values greater than 1, indicating positive relationships among products. The highest confidence value was obtained from the rule Rice and Sugar → Cooking Oil, with a confidence value of 99.32%, while the highest lift value was found in the UHT Milk → Oreo rule, with a lift value of 9.576. These results demonstrate that the Apriori Algorithm can help identify consumer purchasing patterns and support business decision-making through inventory management, product placement, and bundling and cross-selling strategies.]]>
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